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AAAI
2008
13 years 10 months ago
Learning and Inference with Constraints
Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
Ming-Wei Chang, Lev-Arie Ratinov, Nicholas Rizzolo...
ECCV
2010
Springer
14 years 2 days ago
Efficient Highly Over-Complete Sparse Coding using a Mixture Model
Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data...
CHI
2011
ACM
12 years 11 months ago
Creek watch: pairing usefulness and usability for successful citizen science
Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused ...
Sunyoung Kim, Christine Robson, Thomas Zimmerman, ...
IJAR
2010
113views more  IJAR 2010»
13 years 6 months ago
A geometric view on learning Bayesian network structures
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structures, namely to represent every BN structure by a certain (uniquely determined) vector, c...
Milan Studený, Jirí Vomlel, Raymond ...
ICCV
2007
IEEE
14 years 2 months ago
Learning The Discriminative Power-Invariance Trade-Off
We investigate the problem of learning optimal descriptors for a given classification task. Many hand-crafted descriptors have been proposed in the literature for measuring visua...
Manik Varma, Debajyoti Ray